ARTIFICIAL INTELLIGENCE FOR PREDICTION OF SEPSIS IN VERY LOW BIRTH WEIGHT INFANTS

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1 ARTIFICIAL INTELLIGENCE FOR PREDICTION OF SEPSIS IN VERY LOW BIRTH WEIGHT INFANTS Markus Leskinen MD PhD, Neonatologist Children s Hospital, University of Helsinki and Helsinki University Hospital

2 The sepsis case in a nutshell It s all about saving babies with data and artificial intelligence T h e i s s u e Sepsis is common problem in NICUs with severe complications. Detection is difficult: Unspecific and gradual signs. W h a t w e d i d Used available clinical data and advanced analytical methods to identify upcoming sepsis risk. T h e o u t c o m e The model is able to identify sepsis risk 24 hours before a clinician with high and clinically significant accuracy.

3 Neonatal intensive care unit (NICU), Children s Hospital, Helsinki Tertiary hospital serving Southern Finland with population of 1,6 million deliveries per year 29 patient beds, 16 intensive care beds More than 1600 patients per year VLBW infants (BW <1500g) per year Centricity Critical Care (GE Healthcare) patient monitoring system from 1999 > patients > 2000 VLBW infants

4 Ventilator NICU and Data Generation Infusion pumps Patient monitor ino delivery system aeeg monitor

5 Data Streams in NICU Monitoring of vital functions ECG 240 Hz 20,7 million measurements per day Invasive blood pressure measurement 120 Hz Oxygen saturation 60 Hz Temperature Respiratory rate Transcutaneous po2, pco2 Direct connection of ventilators and other medical equipment Laboratory data Manually registered data Data measured by staff Drug prescription Doctors and nurses records

6 Centralized information system Data collection Analysis, visualization Storage Our NICU database 2099 VLBW infants Median gestational age 28+6 weeks, median birth weight 1100 g

7 Sepsis in Newbown Generalized infection with bacteremia Early onset sepsis <72 h of age Pathogens from mother, usually GBS Late onset sepsis, mostly VLBW infants >72 h of age Usually hospital acquired 12% of VLBW infants develop late sepsis during NICU stay Sepsis is associated with high risk of mortality and longterm neurodevelopmental sequelae

8 Diagnostic challenges Unspecific, gradual signs feeding problems, fatigue tachypnea, apneic spells, tachy- or bradycardia No pathognomonic lab test CRP, late response White blood cell count: leukopenia, leukocytosis Blood glucose, metabolic acidosis Blood culture gold standard slow, invasive, false negatives

9 Current management of suspected sepsis Blood culture and (prophylactic) antibiotic therapy for high risk VLBW patients with signs on suspected sepsis duration and drug choice based on result of blood culture and clinical situation Overuse of antibiotics disturbed intestinal microbiome resistence to antibiotics Potential delay in antibiotic therapy because of unspecific signs increased morbidity and mortality

10 Can artificial intelligence be used for early diagnosis of sepsis in VLBW infants? Predictive machine learning models are able to detect events and abnormalities b e f o r e n o t a b l e p a t h o l o g i c a l s y m p t o m s can be observed by conventional means. Our goal was to develop a computational model for p r e d i c t i n g n e o n a t a l s e p s i s using routinely collected patient monitoring data, laboratory results and patient record information.

11 Retrospective sepsis analysis 173 VLBW infants with proven late onset sepsis positive blood culture and clinical diagnosis Control group 1702 VLBW infants without sepsis 106 VLBW infants with clinical suspicion of sepsis, but with negative blood culture Time zero = blood culture Analysis of collected data 48 h prior to blood culture for patterns that could identify sepsis with maximal accuracy 24 h prior to blood culture Monitor data stored as 2 min means Calculations using IBM Watson CHAID decission tree algorithm

12 Measured parameters Monitor data heart rate, respiratory rate, blood pressure, oxygen saturation, temperature, supplemental oxygen 2 min averages of 10 s medians Manual measurements gestational age, sex, birth weight, actual weight, diuresis Lab blood culture, blood glucose, electrolytes, CRP, blood cell count, blood gas analyses

13 Derived parameters Variation in heart rate and temperature during last 10 min and 1 h Variation in respiratory rate during last 10 min Min ja Max temperature, ph, base excess during last 12 h Diuresis (ml/h/kg) during last 12 h Episodes of hypoxia during last 12 h Oxygen saturation/need for additional oxygen Change in mean saturation during last 3 h Cumulative time of hypoxia / total time of treatment Percent time in of hypoxia during last 3 h Ratio and distribution of systolic and diastolic blood pressure

14 Sensitivity and specificity 24 h prior to blood culture At 24 h the prediction model identified blood positive sepsis with 82% sensitivity and ja 96% specificity Positive predictive value 0.88 Negative predictive value 0.94

15 Main parameters used by the prediction model 1. Percentage of time at low oxygen saturation / 3h 2. Arterial PO2 3. Lowest capillary ph / 12h 4. Change in mean saturation during last 3 h 5. Capillary ph 6. Oxygen saturation /need for additional oxygen 7. Capillary PO2 8. Arterial BE 9. White blood cell count 10.Capillary PCO2

16 Timeline of sepsis risk score Time (h) before blood culture Patients with sepsis Controls

17 Conclusions and future development Our algorithm can be identify sepsis in VLBW infants 24 h earlier than regular clinical methods Next step is real time analysis of risk score of sepsis in VLBW infants Web-based tool for clinicians Can other clinical complications in NICU be detected by machine learning? Necrotising enterocolitis (NEC) Retinopathy of prematurity (ROP) Intraventricular hemorrhage (IVH)

18 Collaborators HUS Lastenklinikka Sture Andersson Markus Leskinen IBM Antti Heino Viljami Venekoski Mikko Laakko Maija Väisänen Laura Sutinen

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20 Cohort, n = 2091 Sepsis positive blood culture n = 269 No sepsis positive blood culture n = 1822 Clinical sepsis diagnosis n = 182 No clinical sepsis diagnosis n = 1578 No positive blood culture for candida albicans, candida parapsilosis or yeast n = 182 No positive blood culture for candida albicans, candida parapsilosis or yeast n = 1569 No sepsis within first 72 hours of admission n = 175 Admission time < 180 days n = 173 Admission time < 180 days n = 1558 More than 100 records per patient n = 173 More than 100 records per patient n = 1517 SEPSIS POSITIVE TARGET GROUP REFERENCE GROUP